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I am a Principal Researcher at Microsoft Research AI, in the Information and Data Sciences group.

My current research focuses on causal analysis of large-scale social media timelines, with the vision of making causal question-answering as fast and as common as web search. With hundreds of millions of people publicly reporting on their daily experiences, we can data mine these social media streams to better understand the common and critical situations people are in, the actions they take, and their implications. These inferences are useful for many applications including decision support tools for individuals and analytics to support policy-makers and scientists.

I had a great time presenting our tutorial on social media biases at KDD17, with Alexandra Olteanu. Thanks to the audience for deep, thoughtful questions about research design, bias mitigation, and much more.

Here are our slides (PDF). More details about social media biases and methdological pitfalls in data analysis in our survey paper.

Here are the slides for my tutorial on applying causal inference to social media timelines, presented at the Workshop on Observational Studies in Social Media (OSSM) at ICWSM 2017. The talk emphasizes counterfactual intuitions and highlights common pitfalls. This tutorial steps through the process of analyzing social media timelines with a focus on treatment identification, covariate featurization and outcome analysis applicable to a broad class of conditioned inference algorithms. tutorial_kiciman_ossm17